The prevalence of chronic disease and health-care costs associated with conditions such as diabetes, cardiovascular disease, cancer, and obesity continue to rise nationally and worldwide. One reason is the absence of proper balance between calorie intake and energy expenditure. In addition to balancing calorie intake and physical activity, a special diet for cardiovascular disease and diabetes has been proposed by the American Heart Association. As a result, interventions that help with prevention or self-management of chronic diseases play a central role in reducing health-care costs as well as mortality and morbidity rates.
Self-monitoring is one of the earliest techniques in this field, involving meal recalls and food frequency questionnaires. This conventional technique suffers from several limitations including biases to memory, low adherence, and labor-intensiveness. Other conventional techniques in this area can be divided into three main categories: (1) wearable sensors, (2) computer vision, and (3) smart-phone apps.
Utilizing wearable sensors require individuals to wear specialized devices, which results in limitation in their practicality. A main weakness in using computer vision for diet monitoring is time sensitivity. The image-based nature of this technique requires data recording prior to nutrition intake. Moreover, this technique utilizes existing web images, which do not represent real world data.
The main challenge in nutrition monitoring is to provide users with real-time nutrition intake information, which is not provided by most of the conventional techniques. Although the above technologies provide user with feedback about the nutrition intake, they still require manually recording the data by the end-user.